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Author Virkajärvi, P.; Korhonen, P.; Bellocchi, G.; Curnel, Y.; Wu, L.; Jégo, G.; Persson, T.; Höglind, M.; Van Oijen, M.; Gustavsson, A.-M.; Kipling, R.P.
Title Modelling responses of forages to climate change with a focus on nutritive value Type Journal Article
Year (down) 2016 Publication Advances in Animal Biosciences Abbreviated Journal Advances in Animal Biosciences
Volume 7 Issue 03 Pages 227-228
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ISSN 2040-4700 ISBN Medium
Area Expedition Conference
Notes LiveM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4876
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Author Cammarano, D.; Rivington, M.; Matthew, K.B.; Miller, D.G.; Bellocchi, G.
Title Uncertainties of different weather data input on three multi-models simulations of yield and water use Type Conference Article
Year (down) 2016 Publication Abbreviated Journal
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Publisher Place of Publication Berlin (Germany) Editor
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Area Expedition Conference International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany
Notes Approved no
Call Number MA @ admin @ Serial 4901
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Author Ferrise, R.; Bindi, M.; Acutis, M.; Bellocchi, G.
Title Fuzzy-logic based multi-site crop model evaluation in Europe Type Conference Article
Year (down) 2016 Publication Abbreviated Journal
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Publisher Place of Publication Berlin (Germany) Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium poster
Area Expedition Conference International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany
Notes Approved no
Call Number MA @ admin @ Serial 4916
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Author Bellocchi, G.
Title Fuzzy-logic based multi-site crop model evaluation Type
Year (down) 2015 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 5 Issue Pages Sp5-5
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Abstract The most common way to evaluate simulation models is to quantify the agreement between observations and simulations via statistical metrics such as the root mean squared error and the linear regression coefficient of determination. It is agreed that the aggregation of metrics of different nature intro integrated indicators offers a valuable way to assess models. Expanded notions of model evaluation that have recently emerged, based on the trade-off between properties of the model and agreement between predictions and actual data under contrasting conditions, integrate sensitivity analysis measures and information criteria for model selection, as well as concepts of model robustness, and point to expert judgments to explore the importance of different metrics. As a FACCE MACSUR CropM-LiveM action, a composite indicator (MQIm: Model Quality Indicator for multi-site assessment) was elaborated, by a group of specialists, on metrics commonly used to evaluate crop models (with extension to grassland models) while also integrating aspects of model complexity and stability of performances. The indicator, based on fuzzy bounds applied to a set of weighed metrics, was first revised by a broader group of modellers and then assessed via questionnaire survey of scientists and end-users. We document a crop model evaluation in Europe and assess to what extent the MQIm reflects the main components of model quality and supports inferences about model performances. No Label
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Area Expedition Conference MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK
Notes Approved no
Call Number MA @ admin @ Serial 2120
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Author Bellocchi, G.; Sándor, R.
Title Model intercomparison Type Report
Year (down) 2015 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 6 Issue Pages D-L2.4
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Abstract This deliverable focuses on some illustrative results obtained with different grassland- specific, grassland adapted crop and dynamic vegetation models selected out of the first list of models compiled in D-L2.1.1 to simulate biomass and flux data from grassland sites in Europe and peri-Mediterranean regions (D-L2.1.1 and D-L2.1.2). Results from uncalibrated simulations were documented in the D-L2.3 report as a blind exercise. Some model improvements are emphasized in this report due to the higher information level of the model calibrations. The complete set of results will include simulations from uncalibrated and calibrated models. No Label
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Notes Approved no
Call Number MA @ admin @ Serial 2108
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